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Machine Learning For Dummies
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The no-free-lunch theorem should always be an inspiration for you, reminding you not to fall in love with certain learning approaches just because they brought interesting results in the past. As a good practice, test multiple models, starting with the basic ones — the models that have more bias than variance. You should always favor simple solutions over complex ones. You may discover that a simple solution performs better. For example, you may want to keep things simple and use a linear model instead of a more sophisticated, tree-based ensemble of models.
Representing the performance of different models using the same chart is helpful before choosing the best one to solve your problem. You can place models used to predict consumer behavior, such as a response to a commercial offer, in special gain charts and lift charts. These charts show how your model performs by partitioning its results into deciles or smaller parts. Because you may be interested only...
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